Next Article in Journal
Active Optical Sensors for Tree Stem Detection and Classification in Nurseries
Previous Article in Journal
A Ubiquitous Sensor Network Platform for Integrating Smart Devices into the Semantic Sensor Web
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(6), 10753-10782; doi:10.3390/s140610753

Obstacle Classification and 3D Measurement in Unstructured Environments Based on ToF Cameras

1
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2
Laboratory for Computational Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, USA
3
State Key Laboratory of Robotics, Shenyang 110016, China
4
College of Information Engineering, Xiangtan University, Xiangtan 411105, China
*
Author to whom correspondence should be addressed.
Received: 14 February 2014 / Revised: 30 May 2014 / Accepted: 30 May 2014 / Published: 18 June 2014
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1061 KB, uploaded 21 June 2014]   |  

Abstract

Inspired by the human 3D visual perception system, we present an obstacle detection and classification method based on the use of Time-of-Flight (ToF) cameras for robotic navigation in unstructured environments. The ToF camera provides 3D sensing by capturing an image along with per-pixel 3D space information. Based on this valuable feature and human knowledge of navigation, the proposed method first removes irrelevant regions which do not affect robot’s movement from the scene. In the second step, regions of interest are detected and clustered as possible obstacles using both 3D information and intensity image obtained by the ToF camera. Consequently, a multiple relevance vector machine (RVM) classifier is designed to classify obstacles into four possible classes based on the terrain traversability and geometrical features of the obstacles. Finally, experimental results in various unstructured environments are presented to verify the robustness and performance of the proposed approach. We have found that, compared with the existing obstacle recognition methods, the new approach is more accurate and efficient. View Full-Text
Keywords: mobile robotic navigation; obstacle detection and classification; time-of-flight camera; region of interest detection; unstructured environment perception mobile robotic navigation; obstacle detection and classification; time-of-flight camera; region of interest detection; unstructured environment perception
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yu, H.; Zhu, J.; Wang, Y.; Jia, W.; Sun, M.; Tang, Y. Obstacle Classification and 3D Measurement in Unstructured Environments Based on ToF Cameras. Sensors 2014, 14, 10753-10782.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top